Design of an artificial neural network for the estimation of the flashover voltage on insulators

• Published in: Electric power systems research Vol. 77; no. 12; pp. 1532 - 1540
• Main Authors: Kontargyri, V.T., Gialketsi, A.A., Tsekouras, G.J., Gonos, I.F., Stathopulos, I.A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-10-2007; Elsevier
• Subjects: Applied sciences; Artificial neural network; Connection and protection apparatus; Critical flashover voltage; Electrical engineering. Electrical power engineering; Exact sciences and technology; High voltage insulators; Insulators; Materials; Polluted insulators; Various equipment and components; High voltage insulators; Critical flashover voltage; Artificial neural network; Polluted insulators; Performance evaluation; Dielectric materials; High voltage insulator; Neural network; Critical voltage; Electric discharge; Learning; Salt; Electric insulating material; Electrical flashover; Mathematical model; Train; Form factor
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2006.10.017

This work attempts to apply an artificial neural network in order to estimate the critical flashover voltage on polluted insulators. The artificial neural network uses as input variables the following characteristics of the insulator: diameter, height, creepage distance, form factor and equivalent salt deposit density, and estimates the critical flashover voltage. The data used to train the network and test its performance is derived from experimental measurements and a mathematical model. Various cases have been studied and their results presented separately. Training and testing sets have been modified for each case.